This repository contains implementations of various algorithms taught in the Introduction to Artificial Intelligence class. These algorithms are fundamental in solving problems and making decisions in AI systems.
- Breadth First Search (BFS): Explores all neighbor nodes at the present depth prior to moving on to nodes at the next depth level.
- Depth First Search (DFS): Explores as far as possible along each branch before backtracking.
- Hill Climbing Search: Iteratively makes small changes to the current state in the direction of increasing value.
- Best First Search: Expands the most promising node chosen according to a specified rule.
- A* Search: Combines the strengths of uniform cost search and greedy search, using a heuristic to guide the search process.
The Genetic Algorithm implementation provided here includes features such as population initialization, selection, crossover, mutation, fitness evaluation, and termination. It can be used to solve optimization problems efficiently.
This repository is for educational purposes only so its not longer maintained.